chore: change .dropna() to .iloc[1:] in all expanding risk time series

dropna() is not a good solution as it may loose information
mbk-dev · Jan 25, 2024 · 888a7ac · 888a7ac
1 parent ffd4763
commit 888a7ac
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Showing 3 changed files with 54 additions and 27 deletions.
diff --git a/main.py b/main.py
@@ -33,13 +33,13 @@
 
 # Rolling / Expanding Risk
 al = ok.AssetList(['SPY.US',
-                   'BND.US'
+                   # 'BND.US'
                    ])
 # al.risk_annual.plot()
 #
 # plt.show()
 
-pf = ok.Portfolio(['SPY.US',
-                   'BND.US'
-                   ])
-print(pf.describe())
+# pf = ok.Portfolio(['SPY.US',
+#                    'BND.US'
+#                    ])
+print(type(al.risk_monthly))
diff --git a/okama/asset_list.py b/okama/asset_list.py
@@ -77,7 +77,7 @@ def wealth_indexes(self) -> pd.DataFrame:
         return helpers.Frame.get_wealth_indexes(df)
 
     @property
-    def risk_monthly(self) -> pd.Series:
+    def risk_monthly(self) -> pd.DataFrame:
         """
         Calculate monthly risk expanding time series for each asset.
 
@@ -89,12 +89,12 @@ def risk_monthly(self) -> pd.Series:
 
         Returns
         -------
-        Series
-            Monthly risk (standard deviation) values for each asset in form of Series.
+        DataFrame
+            Monthly risk (standard deviation) expanding time series for each asset in form of Series.
 
         See Also
         --------
-        risk_annual : Calculate annualized risks.
+        risk_annual : Calculate annualized risks expanding time series.
         semideviation_monthly : Calculate semideviation monthly values.
         semideviation_annual : Calculate semideviation annualized values.
         get_var_historic : Calculate historic Value at Risk (VaR).
@@ -105,11 +105,17 @@ def risk_monthly(self) -> pd.Series:
         --------
         >>> al = ok.AssetList(['GC.COMM', 'SHV.US'], ccy='USD', last_date='2021-01')
         >>> al.risk_monthly
-        GC.COMM    0.050864
-        SHV.US     0.001419
-        dtype: float64
+        Symbols   GC.COMM    SHV.US
+        date
+        2007-03  0.025668  0.000141
+        2007-04  0.020872  0.000153
+        2007-05  0.027513  0.000451
+        2007-06  0.025988  0.000406
+                   ...       ...
+        2020-09  0.051006  0.001380
+        2020-10  0.050861  0.001377
         """
-        return self.assets_ror.expanding().std()
+        return self.assets_ror.expanding().std().iloc[1:]
 
     @property
     def risk_annual(self) -> pd.DataFrame:
@@ -123,12 +129,12 @@ def risk_annual(self) -> pd.DataFrame:
 
         Returns
         -------
-        Series
-            Annualized risk (standard deviation) values for each asset in form of Series.
+        DataFrame
+            Annualized risk (standard deviation) expanding time series for each asset.
 
         See Also
         --------
-        risk_monthly : Calculate montly risk for each asset.
+        risk_monthly : Calculate montly risk expanding time series for each asset.
         risk_annual : Calculate annualized risks.
         semideviation_monthly : Calculate semideviation monthly values.
         semideviation_annual : Calculate semideviation annualized values.
@@ -152,7 +158,7 @@ def risk_annual(self) -> pd.DataFrame:
         """
         risk_ts = self.assets_ror.expanding().std()
         mean_return_ts = self.assets_ror.expanding().mean()
-        return helpers.Float.annualize_risk(risk_ts, mean_return_ts).dropna()
+        return helpers.Float.annualize_risk(risk_ts, mean_return_ts).iloc[1:]
 
     @property
     def semideviation_monthly(self) -> pd.Series:

diff --git a/okama/portfolio.py b/okama/portfolio.py
@@ -986,15 +986,15 @@ def real_mean_return(self) -> float:
     @property
     def risk_monthly(self) -> pd.Series:
         """
-        Calculate monthly risk (standard deviation of return) for Portfolio.
+        Calculate monthly risk expanding time series for Portfolio.
 
         Monthly risk of portfolio is a standard deviation of the rate of return time series.
         Standard deviation (sigma σ) is normalized by N-1.
 
         Returns
         -------
-        float
-            Standard deviation value of the monthly return time series.
+        Series
+            Standard deviation of the monthly return expanding time series.
 
         See Also
         --------
@@ -1009,34 +1009,55 @@ def risk_monthly(self) -> pd.Series:
         --------
         >>> pf = ok.Portfolio(['MSFT.US', 'AAPL.US'])
         >>> pf.risk_monthly
-        0.09415483565833212
+        date
+        1986-05    0.020117
+        1986-06    0.122032
+        1986-07    0.130113
+        1986-08    0.116642
+                     ...
+        2023-08    0.092875
+        2023-09    0.092861
+        2023-10    0.092759
+        2023-11    0.092763
+        2023-12    0.092665
+        Freq: M, Name: portfolio_1094.PF, Length: 453, dtype: float64
         """
-        return self.ror.expanding().std()
+        return self.ror.expanding().std().iloc[1:]
 
     @property
     def risk_annual(self) -> pd.Series:
         """
         Calculate annualized risk expanding time series for portfolio.
 
-        Risk in this case is a standard deviation of return.
+        Risk is a standard deviation of the rate of return.
 
         Annualized risk is calculated for rate of retirun time series for the sample from 'first_date' to
         'last_date'.
 
         Returns
         -------
-        float
-            Annualized standard deviation value of the monthly return time series.
+        Series
+            Annualized standard deviation of the monthly return expanding time series.
 
         Examples
         --------
         >>> pf = ok.Portfolio(['MSFT.US', 'AAPL.US'])
         >>> pf.risk_annual
-        0.4374591902169046
+        date
+        1986-05    0.285175
+        1986-06    0.890909
+        1986-07    0.616876
+        1986-08    0.632270
+        1986-09    0.509642
+                     ...
+        2023-08    0.428297
+        2023-09    0.427350
+        2023-10    0.426961
+        2023-11    0.427930
         """
         risk_ts = self.ror.expanding().std()
         mean_return_ts = self.ror.expanding().mean()
-        return helpers.Float.annualize_risk(risk_ts, mean_return_ts).dropna()
+        return helpers.Float.annualize_risk(risk_ts, mean_return_ts).iloc[1:]
 
     @property
     def semideviation_monthly(self) -> float: